The brain is bombarded by sensory information from the world, and must extract certain pieces of useful information using limited neural resources. This means that the brain must be efficient, throwing away information that is not needed in order to focus on the most important part of the sensory information from the world. However, information that is uninformative in one situation may be highly informative in another, and thus this efficiency must be matched by flexibility. One domain where this is particularly true is speech perception, where a noisy, ambiguous sensory signal is mapped onto underlying linguistic units like phonemes, words, and sentences. This mapping changes substantially depending on who is talking. One way the brain might deal with this is to learn talker-specific representations which optimize the efficiency with which speech sounds are processed, and deploy or swap out those representations whenever the talker changes, learning new representations for new talkers as necessary. While there is some evidence that listeners do use such a strategy, little is known about the underlying neural mechanisms. This proposal seeks to clarify these mechanisms through two specific aims. First, functional magnetic resonance imaging (fMRI) will image the brains of listeners while they are hearing words from two talkers with different accents, mixed together. By comparing the areas that are active when the talker switches with areas that are active during periods of learning about each accent (as measured by behavioral responses), the circuits by which listeners learn and deploy talker-specific representations will be elucidated. Second, using multi-voxel pattern analysis techniques, the neural representations of identical speech sounds which have different interpretations depending on the talker will be measured to determine how deeply talker-specific knowledge affects the processing of speech sounds. If talker-specific knowledge is being used to optimize the efficiency of perceptual processing at a low level, then within-category differences should result in more similar patterns of activity, while across-category differences should result in more distinct patterns of activity.